Automatic systems purporting to recognize cursive script writing, or even handwritten characters, have so far met with only limited success. The reason for that can be traced largely to the lack of robustness exhibited by the templates used in the modeling of handwriting. For example, reference is made to U.S. Pat. No. 4,731,857 which describes a three-step procedure for the recognition of run-on handwritten characters. First, potential segmentation points are derived. Second, all combinations of the segments that could reasonably be a character are sent to a character recognizer to obtain ranked choices and corresponding scores. Third, the character sequences are combined so that the best candidate word wins. The recognition algorithm itself is a template matching algorithm based on dynamic programming. Each template is a fully formed character presumably representative of the writer's average way of forming this character, and the elastic matching scores of the current character are computed for each template. This strategy is vulnerable to the extensive variability that can be observed both across writers and across time.
The present invention is directed to devising a fast algorithm for handwriting recognition (i.e., of complexity similar to that of elastic matching as disclosed in U.S. Pat. No. 4,731,857 to Tappert) with an acceptable degree of robustness. This entails at least three crucial specifications: (i) the :feature elements should be chosen such as to characterize handwriting produced in a discrete, run-on, cursive, or unconstrained mode equally well; (ii) these feature elements should be suitably processed so as to minimize redundancy and thereby maximize the information represented on a per-parameter basis; and (iii) the resulting feature parameters should be further analyzed to detect broad trends in the handwriting and enable appropriate modeling of these trends. These specifications are not met by the current elastic matching approach, since (i) it is character-based, and (ii) it simply averages several instances of a character to obtain a character template.
According to the present invention, the signal processing front-end is a great deal more sophisticated than that of elastic matching. Rather than merely chopping the input data into segments, the signal is transformed onto a higher dimensional feature space (chirographic space), whose points represent all raw observations after non-redundant feature extraction. Using a Gaussian (as opposed to Euclidean) measure for a more refined clustering, the prototypes in this space are formed for robustness purposes. Hence, each prototype represents a small building block which may be common to many characters. Instead of character sequences, building block sequences are combined, each of which is assigned a true likelihood defined on a bona fide probability space (as opposed to just a distance score). Finally, the recognition algorithm itself is a maximum a posteriori decoder operating on this probability space. This alternative strategy is better suited to meet specifications for robustness.